Segmentation Model

Segmentation models aim to partition images into meaningful regions, a crucial task across diverse fields like medical imaging and autonomous driving. Current research emphasizes improving model robustness and efficiency, focusing on architectures like U-Nets, Transformers, and diffusion models, often incorporating techniques like continual learning and prompt engineering to adapt to new data or tasks with minimal retraining. These advancements are driving improvements in accuracy and reducing the need for extensive labeled datasets, leading to wider applicability in various scientific and industrial applications.